MSK SPECTRUM data freeze: major cell type embeddings
Florian Uhlitz
2021-01-25
library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(magick, lib.loc = "/home/uhlitzf/miniconda3/lib/R/library")
library(ggpubr)## load global vars:
source("_src/global_vars.R")
# meta_tbl
# clrs
# markers_v6
# markers_v6_super
# cell_type_super_lookup## load full seurat objects with expression data
# seu_obj_tc <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v5/T.cell_processed_filtered_sub.rds")
# seu_obj_cc <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v5/Ovarian.cancer.cell_processed_filtered.rds"))
seu_obj_cc <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_processed_filtered.rds")
markers_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_highqc_markers_02.tsv")
# plot_data <- FetchData(seu_obj_cc, c("sample", grep("^HLA", rownames(seu_obj_cc), value = T))) %>%
# as_tibble() %>%
# gather(gene, value, -sample) %>%
# left_join(meta_tbl, by = "sample") %>%
# group_by(consensus_signature, gene, sample, patient_id_short) %>%
# summarise(value = median(value))
#
# ggplot(plot_data) +
# geom_tile(aes(sample, gene, fill = value)) +
# scale_fill_viridis() +
# facet_grid(~consensus_signature, scales = "free_x", space = "free_x")plot_data <- cbind(cell_id = colnames(seu_obj_cc), FetchData(seu_obj_cc, c("umapharmony_1", "umapharmony_2", "umappca_1", "umappca_2", "RNA_snn_res.0.2", "sample", "cluster_label", grep("pathway", colnames(seu_obj_cc@meta.data), value = T)))) %>%
as_tibble() %>%
left_join(meta_tbl, by = "sample") %>%
filter(!is.na(consensus_signature))
base_umap <- ggplot(plot_data) +
coord_fixed() +
NoAxes() +
theme(legend.position = c(0, 1),
legend.justification = c("left", "top"),
legend.box.just = "left",
legend.margin = margin(1, 1, 1, 1),
#panel.border = element_rect(linetype = 1, color = "black", size = 1),
legend.text = element_text(size = 14, margin = margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5, vjust = 0.5, face = "plain", size = 22))
arrow <- arrow(angle = 20, type = "closed", length = unit(0.1, "npc"))
umap_coord_anno <- ggplot(tibble(group = c("UMAP1", "UMAP2"),
x = c(0, 0), xend = c(1, 0),
y = c(0, 0), yend = c(0, 1),
lx = c(0.5, -0.15), ly = c(-0.15, 0.5),
angle = c(0, 90))) +
geom_segment(aes(x, y, xend = xend, yend = yend, group = group),
arrow = arrow, size = 1, lineend = "round") +
geom_text(aes(lx, ly, label = group, angle = angle), size = 4) +
theme_void() +
coord_fixed(xlim = c(-0.3, 1), ylim = c(-0.3, 1))
add_umap_coord <- function(gg_obj) {
p <- ggdraw() +
draw_plot(gg_obj, x = 0, y = 0, width = 1, height = 1) +
draw_plot(umap_coord_anno, x = -0.015, y = -0.02, width = 0.2, height = 0.2)
return(p)
}pt.size <- 0.1
pt.size2 <- 0.2
pt.size.mini <- 0.01
pt.alpha <- 0.05
pt.alpha.mini <- 0.02
median_tbl <- plot_data %>%
group_by(patient_id_short) %>%
summarise(umappca_1 = median(umappca_1),
umappca_2 = median(umappca_2))
umap_pca_mutsig <- base_umap +
geom_point(aes(umappca_1, umappca_2, color = consensus_signature),
size = pt.size, alpha = pt.alpha) +
geom_text(aes(umappca_1, umappca_2, label = patient_id_short), data = median_tbl) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
labs(title = "Mutational signature")
umap_mutsig <- base_umap +
geom_point(aes(umapharmony_1, umapharmony_2, color = consensus_signature),
size = pt.size2, alpha = pt.alpha) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
guides(color = F) +
labs(title = "")
umap_pca_cluster <- base_umap +
geom_point(aes(umappca_1, umappca_2, color = cluster_label),
size = pt.size, alpha = pt.alpha) +
geom_text(aes(umappca_1, umappca_2, label = patient_id_short), data = median_tbl) +
scale_color_manual(values = clrs$cluster_label$Ovarian.cancer.cell) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
labs(title = "Cluster")
umap_cluster <- base_umap +
geom_point(aes(umapharmony_1, umapharmony_2, color = cluster_label),
size = pt.size2, alpha = pt.alpha) +
scale_color_manual(values = clrs$cluster_label$Ovarian.cancer.cell) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
guides(color = F) +
labs(title = "")
cluster_legend <- cowplot::get_legend(umap_pca_cluster)
umap_pca_jak_stat <- base_umap +
# geom_point(aes(umappca_1, umappca_2), color = "grey80",
# size = pt.size, alpha = pt.alpha,
# data = filter(plot_data, JAK.STAT.pathway <= 0)) +
geom_point(aes(umappca_1, umappca_2, color = JAK.STAT.pathway),
size = pt.size, alpha = pt.alpha,
data = mutate(plot_data, JAK.STAT.pathway = ifelse(JAK.STAT.pathway > 4, 4, JAK.STAT.pathway))) +
geom_text(aes(umappca_1, umappca_2, label = patient_id_short), data = median_tbl) +
scale_color_gradientn(colours = viridis(9), breaks = c(0, 2, 4), limits = c(min(plot_data$JAK.STAT.pathway), 4), labels = c(0, 2, "≥4")) +
labs(title = "JAK-STAT signaling")
umap_jak_stat <- base_umap +
# geom_point(aes(umapharmony_1, umapharmony_2), color = "grey80",
# size = pt.size, alpha = pt.alpha,
# data = filter(plot_data, JAK.STAT.pathway <= 0)) +
geom_point(aes(umapharmony_1, umapharmony_2, color = JAK.STAT.pathway),
size = pt.size2, alpha = pt.alpha,
data = mutate(plot_data, JAK.STAT.pathway = ifelse(JAK.STAT.pathway > 4, 4, JAK.STAT.pathway))) +
scale_color_gradientn(colours = viridis(9), breaks = c(0, 2, 4), limits = c(min(plot_data$JAK.STAT.pathway), 4), labels = c(0, 2, "≥4")) +
guides(color = F) +
labs(title = "")source("_src/comp_plot.R")
cluster_comp <- plot_data %>%
mutate(sort_short_x = str_replace_all(sort_short, "U", "CD45-")) %>%
mutate(sample_id = sample) %>%
group_by(cluster_label, consensus_signature, sample_id, sort_short_x, tumor_supersite, therapy) %>%
tally %>%
group_by(consensus_signature, sample_id, sort_short_x, tumor_supersite, therapy) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup %>%
mutate(cell_type = "Ovarian.cancer.cell")
plist <- default_comp_grid_list(filter(cluster_comp, sort_short_x == "CD45-"),
cluster_label, "Cancer.cell.3", cluster_label,
vec_plot = F, site_box = F,
super_type = "Ovarian.cancer.cell")
pcomp_grid_p_full <- plot_grid(plotlist = plist,
ncol = 1, align = "v",
rel_heights = c(0.25, 0.25, 0.4))
pcomp_grid_p_full
cancer_grid_pca <- ggdraw() +
draw_plot(add_umap_coord(umap_pca_mutsig),
x = 0, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_pca_cluster + guides(color = F)),
x = 0.25, y = 0, width = 0.25, height = 1) +
draw_grob(cluster_legend, x = 0.5, y = -0.15, height = 1) +
draw_plot(add_umap_coord(umap_pca_jak_stat), x = 0.7, y = 0, width = 0.25, height = 1)
cancer_grid <- ggdraw() +
draw_plot(add_umap_coord(umap_mutsig),
x = 0, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_cluster + guides(color = F)),
x = 0.25, y = 0, width = 0.25, height = 1) +
draw_plot(pcomp_grid_p_full,
x = 0.5, y = 0, width = 0.18, height = 1) +
draw_plot(add_umap_coord(umap_jak_stat), x = 0.7, y = 0, width = 0.25, height = 1)
cancer_grid_pca
cancer_grid
# ggsave("_fig/003_cancer_cell/003_umap_grid.pdf", cancer_grid, width = 20, height = 5)
ggsave("_fig/003_cancer_cell/003_umap_grid_pca.png", cancer_grid_pca, width = 20, height = 5)
ggsave("_fig/003_cancer_cell/003_umap_grid.png", cancer_grid, width = 20, height = 5)1 Differential pathway expression in cancer cells
Are there certain pathways activated in cancer cells…
- of patients with high vs low dysfunctional T cells?
- of HRD vs FBI patients
set.seed(42)
sampled_cell_ids <- sample(colnames(seu_obj_cc), 10000)
seu_obj_cc_sub <- subset(seu_obj_cc, cells = sampled_cell_ids)
plot_data <- FetchData(seu_obj_cc, c("umapharmony_1", "umapharmony_2", "sample", "cluster_label", grep("pathway", colnames(seu_obj_cc@meta.data), value = T))) %>%
as_tibble() %>%
gather(pathway, score, -c(1:4)) %>%
left_join(meta_tbl, by = "sample") %>%
filter(sort_short == "CD45-", therapy == "pre-Rx") %>%
mutate(pathway = str_remove_all(pathway, "\\.pathway")) %>%
mutate(cluster_label = str_remove_all(cluster_label, "\\.cell"))
cut_value <- 2
pathway_summary_wrapper <- . %>%
summarise(mean_score = mean(score),
median_score = median(score)) %>%
mutate(median_cut = ifelse(median_score > cut_value, cut_value,
ifelse(median_score < -cut_value, -cut_value,
median_score))) %>%
mutate(mean_cut = ifelse(mean_score > cut_value, cut_value,
ifelse(mean_score < -cut_value, -cut_value,
mean_score)))
# plot_data_summary_patient <- plot_data %>%
# group_by(patient_id_short, consensus_signature, cluster_label,
# pathway, tumor_supersite) %>%
# pathway_summary_wrapper
#
# plot_data_summary_mutsig <- plot_data %>%
# group_by(consensus_signature, pathway) %>%
# pathway_summary_wrapper
#
# plot_data_summary_mutsig_cluster <- plot_data %>%
# group_by(consensus_signature, cluster_label, pathway) %>%
# pathway_summary_wrapper
plot_data_summary_cluster <- plot_data %>%
group_by(cluster_label, pathway) %>%
pathway_summary_wrapper
plot_data_summary_mutsig_patient <- plot_data %>%
group_by(consensus_signature, patient_id_short, pathway) %>%
pathway_summary_wrappercommon_heat_layers <- list(
scale_fill_gradient2(low = scales::muted("blue"), high = scales::muted("red"),
na.value = "grey10",
breaks = c(-cut_value, 0, cut_value),
labels = c(paste0("≤-", cut_value), "0", paste0("≥", cut_value)),
limits = c(-cut_value, cut_value)),
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
plot.margin = margin(0, 0, 0, 0))
)
# ggplot(plot_data_summary_patient) +
# geom_tile(aes(cluster_label, patient_id_short, fill = median_cut)) +
# facet_grid(consensus_signature~pathway, scales = "free", space = "free") +
# common_heat_layers
#
# ggplot(plot_data_summary_mutsig_cluster) +
# geom_tile(aes(pathway, cluster_label, fill = median_cut)) +
# facet_grid(~consensus_signature, scales = "free", space = "free") +
# common_heat_layers
#
# ggplot(plot_data_summary_mutsig) +
# geom_tile(aes(consensus_signature, pathway, fill = median_score)) +
# common_heat_layers
pw_plot1 <- ggplot(mutate(plot_data_summary_cluster, facet_helper = "")) +
geom_tile(aes(cluster_label, pathway, fill = mean_score)) +
common_heat_layers +
facet_grid(~facet_helper, scales = "free", space = "free") +
labs(x = "Cluster", y = "Pathway", fill = "PROGENy\nscore") +
scale_fill_gradient2(low = scales::muted("blue"), high = scales::muted("red"),
na.value = "grey10",
breaks = c(-cut_value, 0, cut_value),
labels = c(paste0("≤-", cut_value), "0", paste0("≥", cut_value))) +
theme(axis.text.x = element_blank())
pw_plot1_anno <- ggplot(mutate(plot_data_summary_cluster, facet_helper = "")) +
geom_tile(aes(cluster_label, facet_helper, fill = cluster_label)) +
common_heat_layers +
scale_fill_manual(values = clrs$cluster_label$Ovarian.cancer.cell %>% setNames(str_remove_all(names(.), "\\.cell"))) +
facet_grid(~facet_helper, scales = "free", space = "free") +
theme(axis.text.y = element_blank(),
strip.text = element_blank()) +
guides(fill = F)
pw_plot2 <- ggplot(plot_data_summary_mutsig_patient) +
geom_tile(aes(patient_id_short, pathway, fill = mean_cut)) +
facet_grid(~consensus_signature, scales = "free", space = "free") +
common_heat_layers +
labs(x = "Patient", y = "", fill = "PROGENy\nscore") +
theme(axis.text.x = element_blank())
pw_plot2_anno <- ggplot(mutate(plot_data_summary_mutsig_patient, facet_helper = "")) +
geom_tile(aes(patient_id_short, facet_helper, fill = consensus_signature)) +
common_heat_layers +
scale_fill_manual(values = clrs$consensus_signature) +
facet_grid(~consensus_signature, scales = "free", space = "free") +
theme(axis.text.y = element_blank(),
strip.text = element_blank()) +
guides(fill = F)
pw_grid <- plot_grid(pw_plot1 + guides(fill = F), ggdraw(), pw_plot2 + guides(fill = F),
pw_plot1_anno, ggdraw(), pw_plot2_anno,
nrow = 2, align = "v", axis = "lrtb",
rel_widths = c(0.25, 0.05, 0.7), rel_heights = c(0.7, 0.3))
pw_boxplot <- filter(plot_data, pathway == "JAK.STAT") %>%
mutate(patient_id_short = ordered(patient_id_short, levels = unique(arrange(plot_data_summary_mutsig_patient, pathway != "JAK.STAT", median_score)$patient_id_short))) %>%
ggplot() +
geom_boxplot(aes(patient_id_short, score, color = consensus_signature)) +
scale_color_manual(values = clrs$consensus_signature) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1)) +
labs(x = "Patient", y = "JAK/STAT pathway\nPROGENy score")
pw_grid_full <- ggdraw() +
draw_plot(pw_grid, x = 0, y = 0.5, width = 0.9, height = 0.5) +
draw_grob(get_legend(pw_plot2), x = 0.92, y = 0.25) +
draw_plot(pw_boxplot + guides(color = F), x = 0, y = 0, width = 0.65, height = 0.45)
pw_grid_full
ggsave("_fig/003_cancer_cell/003_pathway_heatmap.pdf", pw_grid_full,
width = 15, height = 10)2 session info
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-01-25
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## ! package * version date lib
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## stringi 1.5.3 2020-09-09 [1]
## stringr * 1.4.0 2019-02-10 [1]
## survival 3.1-8 2019-12-03 [3]
## testthat 2.3.2 2020-03-02 [1]
## TFisher 0.2.0 2018-03-21 [2]
## TH.data 1.0-10 2019-01-21 [2]
## tibble * 3.0.4 2020-10-12 [1]
## tidyr * 1.1.2 2020-08-27 [1]
## tidyselect 1.1.0 2020-05-11 [1]
## tidyverse * 1.3.0 2019-11-21 [2]
## tsne 0.1-3 2016-07-15 [2]
## usethis 1.5.1 2019-07-04 [2]
## uwot 0.1.5 2019-12-04 [2]
## vctrs 0.3.5 2020-11-17 [1]
## viridis * 0.5.1 2018-03-29 [2]
## viridisLite * 0.3.0 2018-02-01 [2]
## withr 2.3.0 2020-09-22 [1]
## xfun 0.12 2020-01-13 [2]
## xml2 1.3.2 2020-04-23 [1]
## yaml 2.2.1 2020-02-01 [1]
## zip 2.0.4 2019-09-01 [1]
## zoo 1.8-7 2020-01-10 [2]
## source
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
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## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
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## CRAN (R 3.6.2)
## Github (clauswilke/colorblindr@1ac3d4d)
## R-Forge (R 3.6.2)
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## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.2)
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##
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library
##
## P ── Loaded and on-disk path mismatch.